Teaching through learning analyticsPredicting student learning profiles in a physics course at a higher education institution

  1. Elvira G. Rincon-Flores 1
  2. Eunice Lopez-Camacho
  3. Juanjo Mena 1
  4. Omar Olmos 2
  1. 1 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

  2. 2 School of Engineering and Science, Tecnologico de Monterrey
Revista:
IJIMAI

ISSN: 1989-1660

Año de publicación: 2022

Volumen: 7

Número: 7

Páginas: 82-89

Tipo: Artículo

DOI: 10.9781/IJIMAI.2022.01.005 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: IJIMAI

Resumen

Learning Analytics (LA) is increasingly used in Education to set prediction models from artificial intelligence to determine learning profiles. This study aims to determine to what extent K-nearest neighbor and random forest algorithms could become a useful tool for improving the teaching-learning process and reducing academic failure in two Physics courses at the Technological Institute of Monterrey, México (n = 268). A quasi-experimental and mixed method approach was conducted. The main results showed significant differences between the first and second term evaluations in the two groups. One of the main findings of the study is that the predictions were not very accurate for each student in the first term evaluation. However, the predictions became more accurate as the algorithm was fed with larger datasets from the second term evaluation. This result indicates how predictive algorithms based on decision trees, can offer a close approximation to the academic performance that will occur in the class, and this information could be use along with the personal impressions coming from the teacher.

Referencias bibliográficas

  • B. Daniel, “Big data and analytics in higher education: Opportunities and challenges,” in British Journal of Educational Technology, vol. 46, no. 5, pp. 904-920, 2015, doi: 10.1111/bjet.12230
  • F. J. Garcia-Penalvo et al., “Opening learning management systems to personal learning environments,” Journal of Universal Computer Science, vol. 17, no. 9, pp. 1222–1240, 2011.
  • W. Greller and H. Drachsler, “Translating learning into numbers: A generic framework for learning analytics,” Educational Technology Society, vol. 15, no. 3, pp. 42–57, 2012.
  • D. T. Tempelaar, B. Rienties, and B. Giesbers, “In search for the most informative data for feedback generation: Learning analytics in a datarich context,” Computer and Human Behavior, vol. 47, pp. 157–167, 2015, doi: 10.1016/j.chb.2014.05.038
  • C. Vieira, P. Parsons, and V. Byrd, “Visual learning analytics of educational data: A systematic literature review and research agenda,” Computer and Education, vol. 122, no. March, pp. 119–135, 2018, doi: 10.1016/j.compedu.2018.03.018
  • B. T. M. Wong, “Learning analytics in higher education: an analysis of case studies,” Asian Association of Open Universities Journal, vol. 12, no. 1, pp. 21–40, 2017, doi: 10.1108/aaouj-01-2017-0009
  • A. Martínez-Monés et al., “Achievements and challenges in learning analytics in Spain: The view of SNOLA,” RIED. Revista Iberoamericana de Educación a Distancia, vol. 23, no. 2, pp-187-212, 2020. doi: 10.5944/ ried.23.2.26541.
  • Á. Fidalgo-Blanco, M. L. Sein-Echaluce, F. J. García-Peñalvo and M. Á. Conde-González, “Using Learning Analytics to improve teamwork assessment,” Computers in Human Behavior, vol. 47, pp. 149-156, 2015. doi:10.1016/j.chb.2014.11.050.
  • R. F. Arnove, “Imagining what education can be post-COVID-19,” Prospects, vol. 49, no, 1-2, pp. 43-46, 2020. https://doi.org/10.1007/s11125- 020-09474-1
  • S. J. Daniel, “Education and the COVID-19 pandemic,” Prospects, vol. 49, no, 1-2, pp. 91-96, 2020, doi:10.1007/s11125-020-09464-3
  • APA Human behavior in the time of COVID-19: Learning from psychological science, 2020. https://www. psych ologi calsc ience .org/obser ver/human -behav ior-in-thetime-ofcovid -19
  • M. S. C. Thomas and C. Rogers, “Education, the science of learning, and the COVID-19 crisis,” Prospects, vol. 49, no, 1-2, pp. 87-90, 2020, doi:10.1007/s11125-020-09468-z
  • O. Olmos, M. Hernández, E. Avilés, I. Treviño,. “Optimal Paths for academic performance supported by artificial intelligence,” Conference Proceedings of the 6th International Conference on Educational Innovation, CIIE 2018. Monterrey, Mexico, 2018.
  • G. Chirici et al., “A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data,” Remote Sensing of Environment, Elsevier Inc., vol. 176. pp. 282–294, 01-Apr-2016, doi: 10.1016/j.rse.2016.02.001
  • V. K. Ayyadevara, “Random Forest” in: Pro Machine Learning Algorithms,” Berkeley, CA: Apress, 2018, pp 105-116, doi: 10.1007/978-1-4842-3564-5_5
  • Y. Koren, “The bellkor solution to the netflix grand prize,” Netflix Prize Doc., no. August, pp. 1–10, 2009.
  • T. Havens, “Netflix. In From Networks to Netflix” Routledge, pp. 321-331, 2019, doi: 10.4324/9781315658643-30
  • Y. Chen, X. Li, J. Liu, and Z. Ying, “Recommendation System for Adaptive Learning,” Applied Psychological Measurement, vol. 42, no. 1, pp. 24–41, 2018, doi: 10.1177/0146621617697959
  • B. T. Smith, “How adaptive learning really works,” Tech & Learning, vol. 37, no.3, pp. 20-26, 2015.
  • D. Miliband, “Choice and Voice in Personalised Learning,” Pesonalising Education, pp. 9–19, 2006, doi:10.1787/9789264036604-2-en
  • F. J. Gallego-Durán, R. Molina-Carmona, and F. Llorens-Largo, “Measuring the difficulty of activities for adaptive learning,” Universal Access Information Society, vol. 17, no. 2, pp. 335–348, 2018, doi: 10.1007/ s10209-017-0552-x
  • D. L. López, F. V. Muniesa, and Á. V. Gimeno, “Aprendizaje adaptativo en moodle : tres casos prácticos Adaptive learning in moodle : three practical cases,” Education in The Knowledge Society (EKS), vol. 16, pp. 1–12, 2015.
  • D. Gašević, S. Dawson, and G. Siemens, “Aprendizaje adaptativo en moodle : tres casos prácticos Adaptive learning in moodle : three practical cases,” TechTrends, vol. 59, no. 1, 2015.
  • Y. Chen, X. Li, J. Liu, and Z. Ying, “Recommendation System for Adaptive Learning,” Applied Psychological Measurement, vol. 42, no. 1, pp. 24–41, 2018, doi: 10.1177/0146621617697959
  • M. Belgiu and L. Dragu, “Random forest in remote sensing: A review of applications and future directions,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 114, pp. 24–31, 2016, doi: 10.1016/j. isprsjprs.2016.01.011
  • K. Chen, L. Fine, & B. Huberman, “Predicting the Future”. Information Systems Frontiers, vol. 5, no. 1, pp. 47-61, 2003.
  • M. Castañer, O. Camerino, M. T. Anguera, “Métodos mixtos en la investigación de las ciencias de la actividad física y el deporte”. Apuntes Educación Física y Deportes 112, 31-36, 2013.
  • J. W. Creswell, “A concise introduction to mixed methods research”, Thousand Oaks :SAGE, 2015.
  • S. Olmos, J. Mena, E. Torrecilla & A. Iglesias.Olmos. “Improving graduate students learning through the use of Moodle,” Educational Research and Reviews, vol. 10, no. 5, pp. 604-614, 2015.
  • M. Gilliland, “The Business Forecasting Deal,” New Jersey, USA: John Wiley and sons, 2010.
  • O. H. T. Lu, A. Y. Q. Huang, J.C.H. Huang, A. J. Q., Lin, H. Ogata, S. J. H. Yang, “Applying learning analytics for the early prediction of students’ academic performance in blended learning,” Educational Technology and Society, vol. 21, no. 2, pp. 220–232, 2018.doi: 10.2307/26388400
  • B. Szijarto and J. B. Cousins, “Making Space for Adaptive Learning,” American Journal of Evaliation, pp. 1–17, 2018, doi: 10.1177/1098214018781506
  • EduTrends, “Aprendizaje y evaluación adaptativos (adaptive learning and evaluation),” Observatorio de Innovación Educativa del Tecnológico de Monterrey, no. Julio, 2014.
  • F. J. García-Peñalvo, “Learning Analytics as a Breakthrough in Educational Improvement,” in Radical Solutions and Learning Analytics: Personalised Learning and Teaching Through Big Data, D. Burgos, Ed. Lecture Notes in Educational Technology, pp. 1-15, Singapore: Springer Singapore, 2020. doi: 10.1007/978-981-15-4526-9_1.
  • S. Agarwal and D. P. Mukherjee, “Facial expression recognition through adaptive learning of local motion descriptor,” Multimedia Tools and Applications, vol. 76, no. 1, pp. 1073–1099, 2017, doi: 10.1007/s11042-015- 3103-6
  • C. J. Villagrá-Arnedo, F. J. Gallego-Durán, F. Llorens-Largo, R. SatorreCuerda, P. Compañ-Rosique and R. Molina-Carmona, “Time-Dependent Performance Prediction System for Early Insight in Learning Trends,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 6, no. 2, pp. 112-124, 2020. doi: 10.9781/ijimai.2020.05.006.
Fuente de los datos: Dialnet